Clinicalnerpt Pharmacologic

🚀 葡萄牙語臨床命名實體識別 - 藥理學

本藥理學命名實體識別（NER）模型是 BioBERTpt 項目 的一部分，該項目訓練了 13 個臨床實體模型（與統一醫學語言系統 UMLS 兼容）。所有以 "pucpr" 用戶命名的 NER 模型均基於巴西臨床語料庫 SemClinBr 進行訓練，以 BioBERTpt(all) 模型為基礎，訓練 10 個輪次，並採用 IOB2 格式。

示例文本

• “作為出院時開具的心力衰竭藥物治療方案，患者接受呋塞米 40mg，每日兩次；異山梨酯 40mg，每日三次；地高辛 0.25mg/天；卡託普利 50mg，每日三次；螺內酯 25mg/天。”
• “患者正在使用呋塞米 40mg，每日兩次；地高辛 0.25mg/天；辛伐他汀 40mg/晚；卡託普利 50mg，每日三次；異山梨酯 20mg，每日三次；阿司匹林 100mg/天；螺內酯 25mg/天。”

📚 詳細文檔

致謝

本研究部分由巴西高等教育人員素質提升協調局（CAPES）資助 - 資助代碼 001。

引用

@inproceedings{schneider-etal-2020-biobertpt,
    title = "{B}io{BERT}pt - A {P}ortuguese Neural Language Model for Clinical Named Entity Recognition",
    author = "Schneider, Elisa Terumi Rubel  and
      de Souza, Jo{\~a}o Vitor Andrioli  and
      Knafou, Julien  and
      Oliveira, Lucas Emanuel Silva e  and
      Copara, Jenny  and
      Gumiel, Yohan Bonescki  and
      Oliveira, Lucas Ferro Antunes de  and
      Paraiso, Emerson Cabrera  and
      Teodoro, Douglas  and
      Barra, Cl{\'a}udia Maria Cabral Moro",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.7",
    pages = "65--72",
    abstract = "With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72{\%}, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.",
}

常見問題

若有任何問題，請在 BioBERTpt 代碼庫 中提交 GitHub 問題。